Updated meta-analysis of the role of<i>APOE</i>ε2/ε3/ε4 alleles in frontotemporal lobar degeneration
Bibliographic record
Abstract
// Wen-Hua Su 1, 2 , Zhi-Hong Shi 1, 2 , Shu-Ling Liu 1, 2 , Xiao-Dan Wang 1, 2 , Shuai Liu 1, 2 and Yong Ji 1, 2 1 Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China 2 Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Huanhu Hospital, Tianjin, China Correspondence to: Yong Ji, email: jiyongtianjin@163.com Keywords: APOE, FTLD, allele, genotype, meta-analysis Received: January 17, 2017 Accepted: April 11, 2017 Published: April 21, 2017 ABSTRACT We performed an updated meta-analysis to assess the role of the ε2/ε3/ε4 alleles of Apolipoprotein E gene (APOE) in frontotemporal lobar degeneration (FTLD). The relevant articles were retrieved from PubMed, CENTRAL, EMBASE and Web of Science databases, and 51 eligible case-control studies with 5123 cases and 20566 controls were selected after screening according to inclusion and exclusion criteria. Our analysis demonstrated that APOE ε4 was associated with increased FTLD risk in all genetic models (ε4 vs. ε3 allele, ε4 vs. ε2 allele, ε4 vs. ε2+ε3+ε4 allele, ε4 vs. ε2+ε3+ε4 carrier, ε4ε4 vs. ε3ε3, ε3ε4 vs. ε3ε3, ε3ε4+ε4ε4 vs. ε3ε3, ε4ε4 vs. ε3ε3+ε3ε4, all P < 0.01, odds ratio [OR] > 1). Subgroup analysis revealed significant association between APOE ε4 and FTLD ( P < 0.01, OR > 1) for the Caucasian, Italian, population based (PB), P > 0.05 value of the Hardy-Weinberg Equilibrium (HWE), Newcastle-Ottawa scale score > 6, and behavioral variant frontotemporal dementia (bvFTD) subgroups. However, there was no significant association between the APOE ε2 allele and FTLD ( P > 0.05) in most genetic models and sub-group analyses. Begg’s and Egger’s tests also revealed no publication bias, and sensitivity analysis showed that our data analysis was robust. Thus our meta-analyses suggest that APOE ε4 is a genetic risk factor in patients with FTLD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.044 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".